{"cells":[{"cell_type":"code","execution_count":18,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-05-21T01:26:56.627006Z","iopub.status.busy":"2023-05-21T01:26:56.626382Z","iopub.status.idle":"2023-05-21T01:27:00.537833Z","shell.execute_reply":"2023-05-21T01:27:00.535936Z","shell.execute_reply.started":"2023-05-21T01:26:56.626949Z"},"trusted":true},"outputs":[{"ename":"KeyboardInterrupt","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T09:57:01.944701Z","iopub.status.busy":"2023-05-27T09:57:01.944378Z","iopub.status.idle":"2023-05-27T09:57:12.750263Z","shell.execute_reply":"2023-05-27T09:57:12.749294Z","shell.execute_reply.started":"2023-05-27T09:57:01.944673Z"},"trusted":true},"outputs":[],"source":["import torch\n","import torchvision\n","import torchvision.transforms as transforms\n","\n","transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n","batch_size = 32\n","train_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\n","test_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\n","train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n","test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T09:57:12.754067Z","iopub.status.busy":"2023-05-27T09:57:12.753171Z","iopub.status.idle":"2023-05-27T09:57:13.428907Z","shell.execute_reply":"2023-05-27T09:57:13.427688Z","shell.execute_reply.started":"2023-05-27T09:57:12.754032Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["torch.Size([32])\n"]}],"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# functions to show an image\n","\n","\n","def imshow(img):\n","    img = img / 2 + 0.5     # unnormalize\n","    npimg = img.numpy()\n","    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n","    plt.show()\n","\n","\n","# get some random training images\n","dataiter = iter(train_loader)\n","# images, labels = dataiter.next()\n","images, labels = next(dataiter)\n","\n","# show images\n","imshow(torchvision.utils.make_grid(images))\n","# print labels\n","print(labels.shape)"]},{"cell_type":"code","execution_count":133,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T12:00:16.651245Z","iopub.status.busy":"2023-05-27T12:00:16.650854Z","iopub.status.idle":"2023-05-27T12:00:17.078004Z","shell.execute_reply":"2023-05-27T12:00:17.077029Z","shell.execute_reply.started":"2023-05-27T12:00:16.651208Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["ResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace=True)\n","  (layer1): Sequential(\n","    (0): Res2NetBottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Res2NetBottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): Res2NetBottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Res2NetBottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): Res2NetBottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Res2NetBottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): Res2NetBottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Res2NetBottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): ModuleList(\n","        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      )\n","      (bn2): ModuleList(\n","        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (fc): Linear(in_features=2048, out_features=100, bias=True)\n",")\n"]}],"source":["import math\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.nn import init\n","\n","def conv3x3(in_planes, out_planes, stride=1):\n","    \"3x3 convolution with padding\"\n","    return nn.Conv2d(\n","        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n","    )\n","\n","# 原有的LKA架构\n","class LKA(nn.Module):\n","    def __init__(self, dim):\n","        super(LKA, self).__init__()\n","        self.conv0 = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)\n","        self.conv_spatial = nn.Conv2d(dim, dim, 3, stride=1, padding=3, groups=dim, dilation=3)\n","        self.conv1 = nn.Conv2d(dim, dim, 1)\n","\n","\n","    def forward(self, x):\n","        u = x.clone()        \n","        attn = self.conv0(x)\n","        attn1 = self.conv_spatial1(attn)\n","        attn = self.conv1(attn)\n","\n","        return u * attn\n","\n","# 改进后的LKA\n","class sk_LKA(nn.Module):\n","    def __init__(self, dim, WH=32, reduction=1, d=32):\n","        super(sk_LKA, self).__init__()\n","        self.conv0 = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)\n","        self.conv_spatial1 = nn.Conv2d(dim//reduction, dim//reduction, 3, stride=1, padding=3, groups=dim, dilation=3)\n","        self.conv_spatial2 = nn.Conv2d(dim//reduction, dim//reduction, 3, stride=1, padding=5, groups=dim, dilation=5)\n","        self.gap = nn.AvgPool2d(WH)\n","        self.fc = nn.Linear(dim//reduction, d)\n","        self.fc1 = nn.Linear(d, dim//reduction)\n","        self.fc2 = nn.Linear(d, dim//reduction)\n","        self.softmax = nn.Softmax(dim=1)\n","        self.conv1 = nn.Conv2d(dim//reduction, dim, 1)\n","        self.WH = WH\n","\n","\n","    def forward(self, x):\n","        u = x.clone()     \n","#         print(\"x.shape\", x.shape)\n","        attn = self.conv0(x)\n","#         print(\"attn.shape:\", attn.shape)\n","        attn1 = self.conv_spatial1(attn).unsqueeze_(dim=1)\n","#         print(\"attn1.shape:\", attn1.shape)\n","        attns = attn1\n","        attn2 = self.conv_spatial2(attn).unsqueeze_(dim=1)\n","        attns = torch.cat([attns, attn2], dim=1)\n","#         print(\"attns.shape:\", attns.shape)\n","        attn_u = torch.sum(attns, dim=1)\n","#         print(\"attn_u.shape:\", attn_u.shape)\n","        attn_s = self.gap(attn_u).squeeze_()\n","        attn_z = self.fc(attn_s)\n","        vector1 = self.fc1(attn_z).unsqueeze_(dim=1)\n","        attns_vector = vector1\n","        vector2 = self.fc2(attn_z).unsqueeze_(dim=1)\n","        attns_vector = torch.cat([attns_vector, vector2], dim=1)\n","        attns_vector = self.softmax(attns_vector)\n","        attns_vector = attns_vector.unsqueeze(-1).unsqueeze(-1)\n","        attn = (attns*attns_vector).sum(dim=1)\n","        \n","        attn = self.conv1(attn)\n","\n","        return u * attn\n","    \n","# 将LKA与Bottleneck结合在一起\n","class LKABottleneck(nn.Module):\n","    expansion = 4\n","\n","    def __init__(\n","        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, WH=32\n","    ):\n","        super(LKABottleneck, self).__init__()\n","        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n","        self.bn1 = nn.BatchNorm2d(planes)\n","        self.conv2 = nn.Conv2d(\n","            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n","        )\n","        self.bn2 = nn.BatchNorm2d(planes)\n","        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n","        self.bn3 = nn.BatchNorm2d(planes * 4)\n","        self.lka = sk_LKA(planes * 4, WH)\n","        self.relu = nn.ReLU(inplace=True)\n","        self.downsample = downsample\n","        self.stride = stride\n","        self.WH = WH\n","\n","        if use_triplet_attention:\n","            self.triplet_attention = TripletAttention(planes * 4, 16)\n","        else:\n","            self.triplet_attention = None\n","\n","    def forward(self, x):\n","        residual = x\n","\n","        out = self.conv1(x)\n","        out = self.bn1(out)\n","        out = self.relu(out)\n","\n","        out = self.conv2(out)\n","        out = self.bn2(out)\n","        out = self.relu(out)\n","\n","        out = self.conv3(out)\n","        out = self.bn3(out)\n","        \n","        out = self.lka(out)\n","        \n","        if self.downsample is not None:\n","            residual = self.downsample(x)\n","\n","        if not self.triplet_attention is None:\n","            out = self.triplet_attention(out)\n","\n","        out += residual\n","        out = self.relu(out)\n","\n","        return out\n","    \n","# 将LKA嵌入ResNet中\n","class WHResNet(nn.Module):\n","    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n","        self.inplanes = 64\n","        super(WHResNet, self).__init__()\n","        self.network_type = network_type\n","        # different model config between ImageNet and CIFAR\n","        if network_type == \"ImageNet\":\n","            self.conv1 = nn.Conv2d(\n","                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n","            )\n","            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n","            self.avgpool = nn.AvgPool2d(7)\n","        else:\n","            self.conv1 = nn.Conv2d(\n","                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n","            )\n","\n","        self.bn1 = nn.BatchNorm2d(64)\n","        self.relu = nn.ReLU(inplace=True)\n","\n","        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type, WH=32)\n","        self.layer2 = self._make_layer(\n","            block, 128, layers[1], stride=2, att_type=att_type, WH=16\n","        )\n","        self.layer3 = self._make_layer(\n","            block, 256, layers[2], stride=2, att_type=att_type, WH=8\n","        )\n","        self.layer4 = self._make_layer(\n","            block, 512, layers[3], stride=2, att_type=att_type, WH=4\n","        )\n","\n","        self.fc = nn.Linear(512 * block.expansion, num_classes)\n","\n","        init.kaiming_normal_(self.fc.weight)\n","        for key in self.state_dict():\n","            if key.split(\".\")[-1] == \"weight\":\n","                if \"conv\" in key:\n","                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n","                if \"bn\" in key:\n","                    if \"SpatialGate\" in key:\n","                        self.state_dict()[key][...] = 0\n","                    else:\n","                        self.state_dict()[key][...] = 1\n","            elif key.split(\".\")[-1] == \"bias\":\n","                self.state_dict()[key][...] = 0\n","\n","    def _make_layer(self, block, planes, blocks, stride=1, att_type=None, WH=32):\n","        downsample = None\n","        if stride != 1 or self.inplanes != planes * block.expansion:\n","            downsample = nn.Sequential(\n","                nn.Conv2d(\n","                    self.inplanes,\n","                    planes * block.expansion,\n","                    kernel_size=1,\n","                    stride=stride,\n","                    bias=False,\n","                ),\n","                nn.BatchNorm2d(planes * block.expansion),\n","            )\n","\n","        layers = []\n","        layers.append(\n","            block(\n","                self.inplanes,\n","                planes,\n","                stride,\n","                downsample,\n","                use_triplet_attention=att_type == \"TripletAttention\",\n","                WH=WH\n","            )\n","        )\n","        self.inplanes = planes * block.expansion\n","        for i in range(1, blocks):\n","            layers.append(\n","                block(\n","                    self.inplanes,\n","                    planes,\n","                    use_triplet_attention=att_type == \"TripletAttention\",\n","                    WH=WH\n","                )\n","            )\n","\n","        return nn.Sequential(*layers)\n","\n","    def forward(self, x):\n","        x = self.conv1(x)\n","        x = self.bn1(x)\n","        x = self.relu(x)\n","        if self.network_type == \"ImageNet\":\n","            x = self.maxpool(x)\n","\n","        x = self.layer1(x)\n","#         print(\"Begin layer2\")\n","        x = self.layer2(x)\n","        x = self.layer3(x)\n","        x = self.layer4(x)\n","\n","        if self.network_type == \"ImageNet\":\n","            x = self.avgpool(x)\n","        else:\n","            x = F.avg_pool2d(x, 4)\n","        x = x.view(x.size(0), -1)\n","        x = self.fc(x)\n","        return x\n","\n","\n","def SKLKANet(network_type, depth, num_classes, att_type):\n","\n","    assert network_type in [\n","        \"ImageNet\",\n","        \"CIFAR10\",\n","        \"CIFAR100\",\n","    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n","    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n","\n","    if depth == 18:\n","        model = WHResNet(LKABottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n","\n","    return model\n","\n","device=\"cuda\""]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T12:00:18.448292Z","iopub.status.busy":"2023-05-27T12:00:18.447927Z","iopub.status.idle":"2023-05-27T12:00:19.059784Z","shell.execute_reply":"2023-05-27T12:00:19.058662Z","shell.execute_reply.started":"2023-05-27T12:00:18.448257Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["WHResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace=True)\n","  (layer1): Sequential(\n","    (0): LKABottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)\n","        (conv_spatial1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=256)\n","        (conv_spatial2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=256)\n","        (gap): AvgPool2d(kernel_size=32, stride=32, padding=0)\n","        (fc): Linear(in_features=256, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=256, bias=True)\n","        (fc2): Linear(in_features=32, out_features=256, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): LKABottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)\n","        (conv_spatial1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=256)\n","        (conv_spatial2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=256)\n","        (gap): AvgPool2d(kernel_size=32, stride=32, padding=0)\n","        (fc): Linear(in_features=256, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=256, bias=True)\n","        (fc2): Linear(in_features=32, out_features=256, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): LKABottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)\n","        (conv_spatial1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=512)\n","        (conv_spatial2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=512)\n","        (gap): AvgPool2d(kernel_size=16, stride=16, padding=0)\n","        (fc): Linear(in_features=512, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=512, bias=True)\n","        (fc2): Linear(in_features=32, out_features=512, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): LKABottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)\n","        (conv_spatial1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=512)\n","        (conv_spatial2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=512)\n","        (gap): AvgPool2d(kernel_size=16, stride=16, padding=0)\n","        (fc): Linear(in_features=512, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=512, bias=True)\n","        (fc2): Linear(in_features=32, out_features=512, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): LKABottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024)\n","        (conv_spatial1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=1024)\n","        (conv_spatial2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=1024)\n","        (gap): AvgPool2d(kernel_size=8, stride=8, padding=0)\n","        (fc): Linear(in_features=1024, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=1024, bias=True)\n","        (fc2): Linear(in_features=32, out_features=1024, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): LKABottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1024)\n","        (conv_spatial1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=1024)\n","        (conv_spatial2): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=1024)\n","        (gap): AvgPool2d(kernel_size=8, stride=8, padding=0)\n","        (fc): Linear(in_features=1024, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=1024, bias=True)\n","        (fc2): Linear(in_features=32, out_features=1024, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): LKABottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048)\n","        (conv_spatial1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=2048)\n","        (conv_spatial2): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=2048)\n","        (gap): AvgPool2d(kernel_size=4, stride=4, padding=0)\n","        (fc): Linear(in_features=2048, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=2048, bias=True)\n","        (fc2): Linear(in_features=32, out_features=2048, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): LKABottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (lka): ColaLKA(\n","        (conv0): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2048)\n","        (conv_spatial1): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=2048)\n","        (conv_spatial2): Conv2d(2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(5, 5), dilation=(5, 5), groups=2048)\n","        (gap): AvgPool2d(kernel_size=4, stride=4, padding=0)\n","        (fc): Linear(in_features=2048, out_features=32, bias=True)\n","        (fc1): Linear(in_features=32, out_features=2048, bias=True)\n","        (fc2): Linear(in_features=32, out_features=2048, bias=True)\n","        (softmax): Softmax(dim=1)\n","        (conv1): Conv2d(2048, 2048, kernel_size=(1, 1), stride=(1, 1))\n","      )\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (fc): Linear(in_features=2048, out_features=100, bias=True)\n",")\n"]}],"source":["sklka_net = SKLKANet(\"CIFAR100\",18,100,None).to(device)\n","print(sklka_net)"]},{"cell_type":"code","execution_count":138,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T12:00:19.062494Z","iopub.status.busy":"2023-05-27T12:00:19.062110Z","iopub.status.idle":"2023-05-27T12:00:19.071309Z","shell.execute_reply":"2023-05-27T12:00:19.070288Z","shell.execute_reply.started":"2023-05-27T12:00:19.062456Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1250\n"]}],"source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()\n","# 这里需要修改优化器\n","optimizer = torch.optim.Adam(sklka_net.parameters(), lr=0.005)\n","print(len(train_loader))"]},{"cell_type":"code","execution_count":139,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T12:00:19.393247Z","iopub.status.busy":"2023-05-27T12:00:19.392885Z","iopub.status.idle":"2023-05-27T12:00:19.407325Z","shell.execute_reply":"2023-05-27T12:00:19.406338Z","shell.execute_reply.started":"2023-05-27T12:00:19.393217Z"},"trusted":true},"outputs":[],"source":["from tqdm import tqdm\n","def train(net, epoch):\n","    net.train()\n","    # Loop over each batch from the training set\n","    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n","    for batch_idx, (data, target) in enumerate(train_tqdm):\n","        # Copy data to GPU if needed\n","        data = data.to(device)\n","        target = target.to(device)\n","        # Zero gradient buffers\n","        optimizer.zero_grad()\n","        # Pass data through the network\n","        output = net(data)\n","        # Calculate loss\n","        loss = criterion(output, target)\n","        # Backpropagate\n","        loss.backward()\n","        # Update weights\n","        optimizer.step()  # w - alpha * dL / dw\n","        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n","    return net\n","\n","def validate(net, lossv,top1AccuracyList,top5AccuracyList):\n","    net.eval()\n","    val_loss = 0\n","    top1Correct = 0\n","    top5Correct = 0\n","    for index,(data, target) in enumerate(test_loader):\n","        data = data.to(device)\n","        labels = target.to(device)\n","        outputs = net(data)\n","        val_loss += criterion(outputs, labels).data.item()\n","        _, top1Predicted = torch.max(outputs.data, 1)\n","        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n","        top1Correct += (top1Predicted == labels).cpu().sum().item()\n","        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n","        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n","\n","    val_loss /= len(test_loader)\n","    lossv.append(val_loss)\n","\n","    top1Acc=100*top1Correct / len(test_loader.dataset)\n","    top5Acc=100*top5Correct / len(test_loader.dataset)\n","    top1AccuracyList.append(top1Acc)\n","    top5AccuracyList.append(top5Acc)\n","    \n","    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n","        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n","#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n","#     accv.append(accuracy)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2023-05-27T12:36:24.192393Z","iopub.status.busy":"2023-05-27T12:36:24.191970Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["Epoch 0: 100%|██████████| 1250/1250 [02:52<00:00,  7.23it/s, loss=1.78]\n"]},{"name":"stdout","output_type":"stream","text":["\n","Validation set: Average loss: 2.7823, Top1Accuracy: 3608/10000 (36%) Top5Accuracy: 6555.0/10000 (66%)\n","\n"]},{"name":"stderr","output_type":"stream","text":["Epoch 1: 100%|██████████| 1250/1250 [02:52<00:00,  7.26it/s, loss=1.31] \n"]},{"name":"stdout","output_type":"stream","text":["\n","Validation set: Average loss: 2.9399, Top1Accuracy: 3589/10000 (36%) Top5Accuracy: 6519.0/10000 (65%)\n","\n"]},{"name":"stderr","output_type":"stream","text":["Epoch 2: 100%|██████████| 1250/1250 [02:52<00:00,  7.25it/s, loss=1.39] \n"]},{"name":"stdout","output_type":"stream","text":["\n","Validation set: Average loss: 3.2164, Top1Accuracy: 3490/10000 (35%) Top5Accuracy: 6369.0/10000 (64%)\n","\n"]},{"name":"stderr","output_type":"stream","text":["Epoch 3: 100%|██████████| 1250/1250 [02:52<00:00,  7.24it/s, loss=0.513]\n"]},{"name":"stdout","output_type":"stream","text":["\n","Validation set: Average loss: 3.5833, Top1Accuracy: 3413/10000 (34%) Top5Accuracy: 6256.0/10000 (63%)\n","\n"]},{"name":"stderr","output_type":"stream","text":["Epoch 4:  50%|█████     | 625/1250 [01:26<01:25,  7.30it/s, loss=0.488] "]}],"source":["import time\n","\n","sklka_netLossv = []\n","sklka_netTop1AccuracyList = []   # top1准确率列表\n","sklka_netTop5AccuracyList = []   # top5准确率列表\n","max_epoch = 5\n","training_time = 0.0\n","for epoch in range(max_epoch):  # loop over the dataset multiple times\n","    start = time.time()\n","    sklka_net = train(sklka_net, epoch)\n","    end = time.time()\n","    training_time += end-start\n","    with torch.no_grad():\n","        validate(sklka_net, sklka_netLossv,sklka_netTop1AccuracyList,sklka_netTop5AccuracyList)\n","\n","\n","        \n","print('Finished Training')\n","print('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"}},"nbformat":4,"nbformat_minor":4}
